1 Data preparation

1.1 Outline

  • Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded

  • Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.

1.2 Load packages


library(reportfactory)
library(here)
library(rio) 
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)

1.3 Load scripts

These scripts will load:

  • all scripts stored as .R files inside /scripts/
  • all scripts stored as .R files inside /src/

These scripts also contain routines to access the latest clean encrypted data (see next section).


reportfactory::rfh_load_scripts()

1.4 Load clean data

We import the latest NHS pathways data:


x <- import_pathways() %>%
  as_tibble()
x
## # A tibble: 116,283 x 9
##    site_type date       sex    age   ccg_code ccg_name count postcode nhs_region
##    <chr>     <date>     <chr>  <chr> <chr>    <chr>    <int> <chr>    <chr>     
##  1 111       2020-03-18 female 0-18  e380000… nhs_bar…    35 rm13ae   london    
##  2 111       2020-03-18 female 0-18  e380000… nhs_bed…    27 mk454hr  east_of_e…
##  3 111       2020-03-18 female 0-18  e380000… nhs_bla…     9 bb12fd   north_west
##  4 111       2020-03-18 female 0-18  e380000… nhs_bro…    11 br33ql   london    
##  5 111       2020-03-18 female 0-18  e380000… nhs_can…     9 ws111jp  midlands  
##  6 111       2020-03-18 female 0-18  e380000… nhs_cit…    12 n15lz    london    
##  7 111       2020-03-18 female 0-18  e380000… nhs_enf…     7 en40dy   london    
##  8 111       2020-03-18 female 0-18  e380000… nhs_ham…     6 dl62uu   north_eas…
##  9 111       2020-03-18 female 0-18  e380000… nhs_har…    24 ts232la  north_eas…
## 10 111       2020-03-18 female 0-18  e380000… nhs_kin…     6 kt11eu   london    
## # … with 116,273 more rows

We also import demographics data for NHS regions in England, used later in our analysis:


path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
##                  nhs_region variable      value
## 1                North West     0-18 0.22538599
## 2  North East and Yorkshire     0-18 0.21876449
## 3                  Midlands     0-18 0.22564656
## 4           East of England     0-18 0.22810783
## 5                    London     0-18 0.23764782
## 6                South East     0-18 0.22458811
## 7                South West     0-18 0.20799797
## 8                North West    19-69 0.64274078
## 9  North East and Yorkshire    19-69 0.64437753
## 10                 Midlands    19-69 0.63876675
## 11          East of England    19-69 0.63034229
## 12                   London    19-69 0.67820084
## 13               South East    19-69 0.63267336
## 14               South West    19-69 0.63176131
## 15               North West   70-120 0.13187323
## 16 North East and Yorkshire   70-120 0.13685797
## 17                 Midlands   70-120 0.13558669
## 18          East of England   70-120 0.14154988
## 19                   London   70-120 0.08415135
## 20               South East   70-120 0.14273853
## 21               South West   70-120 0.16024072

Finally, we import publically available deaths per NHS region:


dth <- import_deaths() %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

#truncation to account for reporting delay
delay_max <- 21

dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
##     date_report               nhs_region deaths
## 1    2020-03-01          East of England      0
## 2    2020-03-02          East of England      1
## 3    2020-03-03          East of England      0
## 4    2020-03-04          East of England      0
## 5    2020-03-05          East of England      0
## 6    2020-03-06          East of England      1
## 7    2020-03-07          East of England      0
## 8    2020-03-08          East of England      0
## 9    2020-03-09          East of England      1
## 10   2020-03-10          East of England      0
## 11   2020-03-11          East of England      0
## 12   2020-03-12          East of England      0
## 13   2020-03-13          East of England      1
## 14   2020-03-14          East of England      2
## 15   2020-03-15          East of England      2
## 16   2020-03-16          East of England      1
## 17   2020-03-17          East of England      1
## 18   2020-03-18          East of England      5
## 19   2020-03-19          East of England      4
## 20   2020-03-20          East of England      2
## 21   2020-03-21          East of England     11
## 22   2020-03-22          East of England     11
## 23   2020-03-23          East of England     11
## 24   2020-03-24          East of England     19
## 25   2020-03-25          East of England     26
## 26   2020-03-26          East of England     36
## 27   2020-03-27          East of England     38
## 28   2020-03-28          East of England     28
## 29   2020-03-29          East of England     42
## 30   2020-03-30          East of England     45
## 31   2020-03-31          East of England     70
## 32   2020-04-01          East of England     61
## 33   2020-04-02          East of England     64
## 34   2020-04-03          East of England     80
## 35   2020-04-04          East of England     71
## 36   2020-04-05          East of England     76
## 37   2020-04-06          East of England     71
## 38   2020-04-07          East of England     92
## 39   2020-04-08          East of England    111
## 40   2020-04-09          East of England     87
## 41   2020-04-10          East of England     74
## 42   2020-04-11          East of England     91
## 43   2020-04-12          East of England    101
## 44   2020-04-13          East of England     77
## 45   2020-04-14          East of England     61
## 46   2020-04-15          East of England     82
## 47   2020-04-16          East of England     74
## 48   2020-04-17          East of England     86
## 49   2020-04-18          East of England     63
## 50   2020-04-19          East of England     66
## 51   2020-04-20          East of England     66
## 52   2020-04-21          East of England     74
## 53   2020-04-22          East of England     66
## 54   2020-04-23          East of England     49
## 55   2020-04-24          East of England     64
## 56   2020-04-25          East of England     54
## 57   2020-04-26          East of England     48
## 58   2020-04-27          East of England     46
## 59   2020-04-28          East of England     58
## 60   2020-04-29          East of England     32
## 61   2020-04-30          East of England     43
## 62   2020-05-01          East of England     49
## 63   2020-05-02          East of England     29
## 64   2020-05-03          East of England     41
## 65   2020-05-04          East of England     19
## 66   2020-05-05          East of England     35
## 67   2020-05-06          East of England     28
## 68   2020-05-07          East of England     33
## 69   2020-05-08          East of England     30
## 70   2020-05-09          East of England     26
## 71   2020-05-10          East of England     21
## 72   2020-05-11          East of England     18
## 73   2020-05-12          East of England     21
## 74   2020-05-13          East of England     27
## 75   2020-05-14          East of England     25
## 76   2020-05-15          East of England     18
## 77   2020-05-16          East of England     24
## 78   2020-05-17          East of England     15
## 79   2020-05-18          East of England     16
## 80   2020-05-19          East of England     14
## 81   2020-05-20          East of England     21
## 82   2020-05-21          East of England     17
## 83   2020-05-22          East of England      7
## 84   2020-05-23          East of England      2
## 85   2020-03-01                   London      0
## 86   2020-03-02                   London      0
## 87   2020-03-03                   London      0
## 88   2020-03-04                   London      0
## 89   2020-03-05                   London      0
## 90   2020-03-06                   London      1
## 91   2020-03-07                   London      1
## 92   2020-03-08                   London      0
## 93   2020-03-09                   London      1
## 94   2020-03-10                   London      0
## 95   2020-03-11                   London      7
## 96   2020-03-12                   London      6
## 97   2020-03-13                   London     10
## 98   2020-03-14                   London     14
## 99   2020-03-15                   London     10
## 100  2020-03-16                   London     17
## 101  2020-03-17                   London     25
## 102  2020-03-18                   London     31
## 103  2020-03-19                   London     25
## 104  2020-03-20                   London     45
## 105  2020-03-21                   London     49
## 106  2020-03-22                   London     54
## 107  2020-03-23                   London     63
## 108  2020-03-24                   London     86
## 109  2020-03-25                   London    112
## 110  2020-03-26                   London    130
## 111  2020-03-27                   London    129
## 112  2020-03-28                   London    122
## 113  2020-03-29                   London    147
## 114  2020-03-30                   London    148
## 115  2020-03-31                   London    180
## 116  2020-04-01                   London    201
## 117  2020-04-02                   London    189
## 118  2020-04-03                   London    196
## 119  2020-04-04                   London    229
## 120  2020-04-05                   London    194
## 121  2020-04-06                   London    198
## 122  2020-04-07                   London    219
## 123  2020-04-08                   London    236
## 124  2020-04-09                   London    202
## 125  2020-04-10                   London    168
## 126  2020-04-11                   London    175
## 127  2020-04-12                   London    156
## 128  2020-04-13                   London    165
## 129  2020-04-14                   London    142
## 130  2020-04-15                   London    142
## 131  2020-04-16                   London    138
## 132  2020-04-17                   London     99
## 133  2020-04-18                   London    101
## 134  2020-04-19                   London    102
## 135  2020-04-20                   London     94
## 136  2020-04-21                   London     93
## 137  2020-04-22                   London    108
## 138  2020-04-23                   London     77
## 139  2020-04-24                   London     71
## 140  2020-04-25                   London     57
## 141  2020-04-26                   London     53
## 142  2020-04-27                   London     51
## 143  2020-04-28                   London     43
## 144  2020-04-29                   London     43
## 145  2020-04-30                   London     39
## 146  2020-05-01                   London     41
## 147  2020-05-02                   London     40
## 148  2020-05-03                   London     35
## 149  2020-05-04                   London     29
## 150  2020-05-05                   London     25
## 151  2020-05-06                   London     34
## 152  2020-05-07                   London     35
## 153  2020-05-08                   London     29
## 154  2020-05-09                   London     22
## 155  2020-05-10                   London     25
## 156  2020-05-11                   London     16
## 157  2020-05-12                   London     16
## 158  2020-05-13                   London     16
## 159  2020-05-14                   London     20
## 160  2020-05-15                   London     17
## 161  2020-05-16                   London     13
## 162  2020-05-17                   London     15
## 163  2020-05-18                   London      9
## 164  2020-05-19                   London     12
## 165  2020-05-20                   London     18
## 166  2020-05-21                   London     10
## 167  2020-05-22                   London      5
## 168  2020-05-23                   London      2
## 169  2020-03-01                 Midlands      0
## 170  2020-03-02                 Midlands      0
## 171  2020-03-03                 Midlands      1
## 172  2020-03-04                 Midlands      0
## 173  2020-03-05                 Midlands      0
## 174  2020-03-06                 Midlands      0
## 175  2020-03-07                 Midlands      0
## 176  2020-03-08                 Midlands      3
## 177  2020-03-09                 Midlands      1
## 178  2020-03-10                 Midlands      0
## 179  2020-03-11                 Midlands      2
## 180  2020-03-12                 Midlands      6
## 181  2020-03-13                 Midlands      5
## 182  2020-03-14                 Midlands      4
## 183  2020-03-15                 Midlands      5
## 184  2020-03-16                 Midlands     11
## 185  2020-03-17                 Midlands      8
## 186  2020-03-18                 Midlands     13
## 187  2020-03-19                 Midlands      8
## 188  2020-03-20                 Midlands     28
## 189  2020-03-21                 Midlands     13
## 190  2020-03-22                 Midlands     31
## 191  2020-03-23                 Midlands     33
## 192  2020-03-24                 Midlands     41
## 193  2020-03-25                 Midlands     48
## 194  2020-03-26                 Midlands     64
## 195  2020-03-27                 Midlands     72
## 196  2020-03-28                 Midlands     89
## 197  2020-03-29                 Midlands     92
## 198  2020-03-30                 Midlands     90
## 199  2020-03-31                 Midlands    123
## 200  2020-04-01                 Midlands    140
## 201  2020-04-02                 Midlands    142
## 202  2020-04-03                 Midlands    124
## 203  2020-04-04                 Midlands    150
## 204  2020-04-05                 Midlands    164
## 205  2020-04-06                 Midlands    140
## 206  2020-04-07                 Midlands    123
## 207  2020-04-08                 Midlands    185
## 208  2020-04-09                 Midlands    138
## 209  2020-04-10                 Midlands    127
## 210  2020-04-11                 Midlands    142
## 211  2020-04-12                 Midlands    138
## 212  2020-04-13                 Midlands    120
## 213  2020-04-14                 Midlands    116
## 214  2020-04-15                 Midlands    147
## 215  2020-04-16                 Midlands    101
## 216  2020-04-17                 Midlands    118
## 217  2020-04-18                 Midlands    115
## 218  2020-04-19                 Midlands     91
## 219  2020-04-20                 Midlands    107
## 220  2020-04-21                 Midlands     86
## 221  2020-04-22                 Midlands     77
## 222  2020-04-23                 Midlands    102
## 223  2020-04-24                 Midlands     77
## 224  2020-04-25                 Midlands     72
## 225  2020-04-26                 Midlands     81
## 226  2020-04-27                 Midlands     74
## 227  2020-04-28                 Midlands     68
## 228  2020-04-29                 Midlands     53
## 229  2020-04-30                 Midlands     53
## 230  2020-05-01                 Midlands     64
## 231  2020-05-02                 Midlands     51
## 232  2020-05-03                 Midlands     50
## 233  2020-05-04                 Midlands     60
## 234  2020-05-05                 Midlands     58
## 235  2020-05-06                 Midlands     56
## 236  2020-05-07                 Midlands     48
## 237  2020-05-08                 Midlands     34
## 238  2020-05-09                 Midlands     37
## 239  2020-05-10                 Midlands     41
## 240  2020-05-11                 Midlands     32
## 241  2020-05-12                 Midlands     45
## 242  2020-05-13                 Midlands     38
## 243  2020-05-14                 Midlands     32
## 244  2020-05-15                 Midlands     38
## 245  2020-05-16                 Midlands     34
## 246  2020-05-17                 Midlands     30
## 247  2020-05-18                 Midlands     33
## 248  2020-05-19                 Midlands     31
## 249  2020-05-20                 Midlands     31
## 250  2020-05-21                 Midlands     26
## 251  2020-05-22                 Midlands     14
## 252  2020-05-23                 Midlands      7
## 253  2020-03-01 North East and Yorkshire      0
## 254  2020-03-02 North East and Yorkshire      0
## 255  2020-03-03 North East and Yorkshire      0
## 256  2020-03-04 North East and Yorkshire      0
## 257  2020-03-05 North East and Yorkshire      0
## 258  2020-03-06 North East and Yorkshire      0
## 259  2020-03-07 North East and Yorkshire      0
## 260  2020-03-08 North East and Yorkshire      0
## 261  2020-03-09 North East and Yorkshire      0
## 262  2020-03-10 North East and Yorkshire      0
## 263  2020-03-11 North East and Yorkshire      0
## 264  2020-03-12 North East and Yorkshire      0
## 265  2020-03-13 North East and Yorkshire      0
## 266  2020-03-14 North East and Yorkshire      0
## 267  2020-03-15 North East and Yorkshire      2
## 268  2020-03-16 North East and Yorkshire      3
## 269  2020-03-17 North East and Yorkshire      1
## 270  2020-03-18 North East and Yorkshire      2
## 271  2020-03-19 North East and Yorkshire      6
## 272  2020-03-20 North East and Yorkshire      5
## 273  2020-03-21 North East and Yorkshire      6
## 274  2020-03-22 North East and Yorkshire      7
## 275  2020-03-23 North East and Yorkshire      9
## 276  2020-03-24 North East and Yorkshire      7
## 277  2020-03-25 North East and Yorkshire     18
## 278  2020-03-26 North East and Yorkshire     21
## 279  2020-03-27 North East and Yorkshire     28
## 280  2020-03-28 North East and Yorkshire     35
## 281  2020-03-29 North East and Yorkshire     38
## 282  2020-03-30 North East and Yorkshire     64
## 283  2020-03-31 North East and Yorkshire     60
## 284  2020-04-01 North East and Yorkshire     67
## 285  2020-04-02 North East and Yorkshire     74
## 286  2020-04-03 North East and Yorkshire     99
## 287  2020-04-04 North East and Yorkshire    104
## 288  2020-04-05 North East and Yorkshire     92
## 289  2020-04-06 North East and Yorkshire     95
## 290  2020-04-07 North East and Yorkshire    102
## 291  2020-04-08 North East and Yorkshire    107
## 292  2020-04-09 North East and Yorkshire    111
## 293  2020-04-10 North East and Yorkshire    117
## 294  2020-04-11 North East and Yorkshire     98
## 295  2020-04-12 North East and Yorkshire     84
## 296  2020-04-13 North East and Yorkshire     94
## 297  2020-04-14 North East and Yorkshire    107
## 298  2020-04-15 North East and Yorkshire     95
## 299  2020-04-16 North East and Yorkshire    103
## 300  2020-04-17 North East and Yorkshire     86
## 301  2020-04-18 North East and Yorkshire     95
## 302  2020-04-19 North East and Yorkshire     87
## 303  2020-04-20 North East and Yorkshire    100
## 304  2020-04-21 North East and Yorkshire     76
## 305  2020-04-22 North East and Yorkshire     83
## 306  2020-04-23 North East and Yorkshire     62
## 307  2020-04-24 North East and Yorkshire     72
## 308  2020-04-25 North East and Yorkshire     68
## 309  2020-04-26 North East and Yorkshire     63
## 310  2020-04-27 North East and Yorkshire     65
## 311  2020-04-28 North East and Yorkshire     57
## 312  2020-04-29 North East and Yorkshire     69
## 313  2020-04-30 North East and Yorkshire     56
## 314  2020-05-01 North East and Yorkshire     64
## 315  2020-05-02 North East and Yorkshire     48
## 316  2020-05-03 North East and Yorkshire     39
## 317  2020-05-04 North East and Yorkshire     48
## 318  2020-05-05 North East and Yorkshire     40
## 319  2020-05-06 North East and Yorkshire     50
## 320  2020-05-07 North East and Yorkshire     41
## 321  2020-05-08 North East and Yorkshire     38
## 322  2020-05-09 North East and Yorkshire     43
## 323  2020-05-10 North East and Yorkshire     39
## 324  2020-05-11 North East and Yorkshire     28
## 325  2020-05-12 North East and Yorkshire     25
## 326  2020-05-13 North East and Yorkshire     27
## 327  2020-05-14 North East and Yorkshire     28
## 328  2020-05-15 North East and Yorkshire     30
## 329  2020-05-16 North East and Yorkshire     35
## 330  2020-05-17 North East and Yorkshire     26
## 331  2020-05-18 North East and Yorkshire     26
## 332  2020-05-19 North East and Yorkshire     27
## 333  2020-05-20 North East and Yorkshire     20
## 334  2020-05-21 North East and Yorkshire     29
## 335  2020-05-22 North East and Yorkshire     19
## 336  2020-05-23 North East and Yorkshire      8
## 337  2020-03-01               North West      0
## 338  2020-03-02               North West      0
## 339  2020-03-03               North West      0
## 340  2020-03-04               North West      0
## 341  2020-03-05               North West      1
## 342  2020-03-06               North West      0
## 343  2020-03-07               North West      0
## 344  2020-03-08               North West      1
## 345  2020-03-09               North West      0
## 346  2020-03-10               North West      0
## 347  2020-03-11               North West      0
## 348  2020-03-12               North West      2
## 349  2020-03-13               North West      3
## 350  2020-03-14               North West      1
## 351  2020-03-15               North West      4
## 352  2020-03-16               North West      2
## 353  2020-03-17               North West      4
## 354  2020-03-18               North West      6
## 355  2020-03-19               North West      6
## 356  2020-03-20               North West     10
## 357  2020-03-21               North West     11
## 358  2020-03-22               North West     13
## 359  2020-03-23               North West     15
## 360  2020-03-24               North West     21
## 361  2020-03-25               North West     20
## 362  2020-03-26               North West     29
## 363  2020-03-27               North West     35
## 364  2020-03-28               North West     27
## 365  2020-03-29               North West     46
## 366  2020-03-30               North West     66
## 367  2020-03-31               North West     52
## 368  2020-04-01               North West     85
## 369  2020-04-02               North West     95
## 370  2020-04-03               North West     94
## 371  2020-04-04               North West     98
## 372  2020-04-05               North West    102
## 373  2020-04-06               North West    100
## 374  2020-04-07               North West    133
## 375  2020-04-08               North West    123
## 376  2020-04-09               North West    118
## 377  2020-04-10               North West    115
## 378  2020-04-11               North West    135
## 379  2020-04-12               North West    126
## 380  2020-04-13               North West    125
## 381  2020-04-14               North West    130
## 382  2020-04-15               North West    114
## 383  2020-04-16               North West    133
## 384  2020-04-17               North West     96
## 385  2020-04-18               North West    112
## 386  2020-04-19               North West     70
## 387  2020-04-20               North West     80
## 388  2020-04-21               North West     75
## 389  2020-04-22               North West     80
## 390  2020-04-23               North West     85
## 391  2020-04-24               North West     65
## 392  2020-04-25               North West     65
## 393  2020-04-26               North West     54
## 394  2020-04-27               North West     54
## 395  2020-04-28               North West     56
## 396  2020-04-29               North West     62
## 397  2020-04-30               North West     57
## 398  2020-05-01               North West     43
## 399  2020-05-02               North West     55
## 400  2020-05-03               North West     54
## 401  2020-05-04               North West     44
## 402  2020-05-05               North West     46
## 403  2020-05-06               North West     41
## 404  2020-05-07               North West     44
## 405  2020-05-08               North West     40
## 406  2020-05-09               North West     28
## 407  2020-05-10               North West     38
## 408  2020-05-11               North West     32
## 409  2020-05-12               North West     35
## 410  2020-05-13               North West     24
## 411  2020-05-14               North West     26
## 412  2020-05-15               North West     33
## 413  2020-05-16               North West     30
## 414  2020-05-17               North West     23
## 415  2020-05-18               North West     26
## 416  2020-05-19               North West     31
## 417  2020-05-20               North West     23
## 418  2020-05-21               North West     20
## 419  2020-05-22               North West     13
## 420  2020-05-23               North West      7
## 421  2020-03-01               South East      0
## 422  2020-03-02               South East      0
## 423  2020-03-03               South East      1
## 424  2020-03-04               South East      0
## 425  2020-03-05               South East      1
## 426  2020-03-06               South East      0
## 427  2020-03-07               South East      0
## 428  2020-03-08               South East      1
## 429  2020-03-09               South East      1
## 430  2020-03-10               South East      1
## 431  2020-03-11               South East      1
## 432  2020-03-12               South East      0
## 433  2020-03-13               South East      1
## 434  2020-03-14               South East      1
## 435  2020-03-15               South East      5
## 436  2020-03-16               South East      8
## 437  2020-03-17               South East      7
## 438  2020-03-18               South East     10
## 439  2020-03-19               South East      9
## 440  2020-03-20               South East     13
## 441  2020-03-21               South East      7
## 442  2020-03-22               South East     25
## 443  2020-03-23               South East     20
## 444  2020-03-24               South East     22
## 445  2020-03-25               South East     28
## 446  2020-03-26               South East     34
## 447  2020-03-27               South East     34
## 448  2020-03-28               South East     36
## 449  2020-03-29               South East     54
## 450  2020-03-30               South East     58
## 451  2020-03-31               South East     65
## 452  2020-04-01               South East     65
## 453  2020-04-02               South East     55
## 454  2020-04-03               South East     72
## 455  2020-04-04               South East     80
## 456  2020-04-05               South East     81
## 457  2020-04-06               South East     87
## 458  2020-04-07               South East     99
## 459  2020-04-08               South East     82
## 460  2020-04-09               South East    104
## 461  2020-04-10               South East     88
## 462  2020-04-11               South East     87
## 463  2020-04-12               South East     88
## 464  2020-04-13               South East     83
## 465  2020-04-14               South East     64
## 466  2020-04-15               South East     72
## 467  2020-04-16               South East     56
## 468  2020-04-17               South East     86
## 469  2020-04-18               South East     57
## 470  2020-04-19               South East     69
## 471  2020-04-20               South East     85
## 472  2020-04-21               South East     49
## 473  2020-04-22               South East     54
## 474  2020-04-23               South East     57
## 475  2020-04-24               South East     64
## 476  2020-04-25               South East     50
## 477  2020-04-26               South East     51
## 478  2020-04-27               South East     40
## 479  2020-04-28               South East     40
## 480  2020-04-29               South East     46
## 481  2020-04-30               South East     28
## 482  2020-05-01               South East     37
## 483  2020-05-02               South East     35
## 484  2020-05-03               South East     17
## 485  2020-05-04               South East     35
## 486  2020-05-05               South East     29
## 487  2020-05-06               South East     22
## 488  2020-05-07               South East     25
## 489  2020-05-08               South East     25
## 490  2020-05-09               South East     28
## 491  2020-05-10               South East     19
## 492  2020-05-11               South East     23
## 493  2020-05-12               South East     26
## 494  2020-05-13               South East     17
## 495  2020-05-14               South East     31
## 496  2020-05-15               South East     23
## 497  2020-05-16               South East     18
## 498  2020-05-17               South East     16
## 499  2020-05-18               South East     17
## 500  2020-05-19               South East     12
## 501  2020-05-20               South East     21
## 502  2020-05-21               South East     10
## 503  2020-05-22               South East      8
## 504  2020-05-23               South East      1
## 505  2020-03-01               South West      0
## 506  2020-03-02               South West      0
## 507  2020-03-03               South West      0
## 508  2020-03-04               South West      0
## 509  2020-03-05               South West      0
## 510  2020-03-06               South West      0
## 511  2020-03-07               South West      0
## 512  2020-03-08               South West      0
## 513  2020-03-09               South West      0
## 514  2020-03-10               South West      0
## 515  2020-03-11               South West      1
## 516  2020-03-12               South West      0
## 517  2020-03-13               South West      0
## 518  2020-03-14               South West      1
## 519  2020-03-15               South West      0
## 520  2020-03-16               South West      0
## 521  2020-03-17               South West      2
## 522  2020-03-18               South West      2
## 523  2020-03-19               South West      4
## 524  2020-03-20               South West      3
## 525  2020-03-21               South West      6
## 526  2020-03-22               South West      9
## 527  2020-03-23               South West      9
## 528  2020-03-24               South West      7
## 529  2020-03-25               South West      9
## 530  2020-03-26               South West     11
## 531  2020-03-27               South West     13
## 532  2020-03-28               South West     21
## 533  2020-03-29               South West     18
## 534  2020-03-30               South West     23
## 535  2020-03-31               South West     23
## 536  2020-04-01               South West     22
## 537  2020-04-02               South West     23
## 538  2020-04-03               South West     30
## 539  2020-04-04               South West     42
## 540  2020-04-05               South West     32
## 541  2020-04-06               South West     34
## 542  2020-04-07               South West     39
## 543  2020-04-08               South West     47
## 544  2020-04-09               South West     24
## 545  2020-04-10               South West     46
## 546  2020-04-11               South West     43
## 547  2020-04-12               South West     23
## 548  2020-04-13               South West     26
## 549  2020-04-14               South West     24
## 550  2020-04-15               South West     31
## 551  2020-04-16               South West     29
## 552  2020-04-17               South West     33
## 553  2020-04-18               South West     25
## 554  2020-04-19               South West     31
## 555  2020-04-20               South West     26
## 556  2020-04-21               South West     26
## 557  2020-04-22               South West     22
## 558  2020-04-23               South West     17
## 559  2020-04-24               South West     19
## 560  2020-04-25               South West     15
## 561  2020-04-26               South West     27
## 562  2020-04-27               South West     13
## 563  2020-04-28               South West     17
## 564  2020-04-29               South West     14
## 565  2020-04-30               South West     26
## 566  2020-05-01               South West      6
## 567  2020-05-02               South West      6
## 568  2020-05-03               South West     10
## 569  2020-05-04               South West     16
## 570  2020-05-05               South West     14
## 571  2020-05-06               South West     18
## 572  2020-05-07               South West     16
## 573  2020-05-08               South West      5
## 574  2020-05-09               South West     10
## 575  2020-05-10               South West      5
## 576  2020-05-11               South West      7
## 577  2020-05-12               South West      7
## 578  2020-05-13               South West      7
## 579  2020-05-14               South West      6
## 580  2020-05-15               South West      3
## 581  2020-05-16               South West      4
## 582  2020-05-17               South West      6
## 583  2020-05-18               South West      4
## 584  2020-05-19               South West      5
## 585  2020-05-20               South West      1
## 586  2020-05-21               South West      8
## 587  2020-05-22               South West      4
## 588  2020-05-23               South West      1

1.5 Completion date

We extract the completion date from the NHS Pathways file timestamp:


database_date <- attr(x, "timestamp")
database_date
## [1] "2020-05-21"

The completion date of the NHS Pathways data is Thursday 21 May 2020.

1.6 Add variables

We add the following variable:

  • day: an integer representing the number of days from the earliest data reported, used for modelling purposes; the first day is 0

x <- x %>% 
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)),
         nhs_region = gsub(" Of ", " of ", nhs_region),
         nhs_region = gsub(" And ", " and ", nhs_region),
         day = as.integer(date - min(date, na.rm = TRUE)))

1.7 Auxiliary functions

These are functions which will be used further in the analyses.

Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:


## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here

Rsq <- function(x) {
  1 - (x$deviance / x$null.deviance)
}

Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:


## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals

get_r <- function(model) {
  ##  extract coefficients and conf int
  out <- data.frame(r = coef(model))  %>%
    rownames_to_column("var") %>% 
    cbind(confint(model)) %>%
    filter(!grepl("day_of_week", var)) %>% 
    filter(grepl("day", var)) %>%
    rename(lower_95 = "2.5 %",
           upper_95 = "97.5 %") %>%
    mutate(var = sub("day:", "", var))
  
  ## reconstruct values: intercept + region-coefficient
  for (i in 2:nrow(out)) {
    out[i, -1] <- out[1, -1] + out[i, -1]
  }
  
  ## find the name of the intercept, restore regions names
  out <- out %>%
    mutate(nhs_region = model$xlevels$nhs_region) %>%
    select(nhs_region, everything(), -var)
  
  ## find halving times
  halving <- log(0.5) / out[,-1] %>%
    rename(halving_t = r,
           halving_t_lower_95 = lower_95,
           halving_t_upper_95 = upper_95)
  
  ## set halving times with exclusion intervals to NA
  no_halving <- out$lower_95 < 0 & out$upper_95 > 0
  halving[no_halving, ] <- NA_real_
  
  ## return all data
  cbind(out, halving)
  
}

Functions used in the correlation analysis between NHS Pathways reports and deaths:

## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.

getcor <- function(x, ndx) {
  return(cor(x$deaths[ndx],
             x$note_lag[ndx],
             use = "complete.obs",
             method = "pearson"))
}

## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)

getboot <- function(x) {
  result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000), 
                           type = "bca")
  return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
                    r = result$t0,
                    r_low = result$bca[4],
                    r_hi = result$bca[5]))
}

Function to classify the day of the week into weekend, Monday, and the rest:


## Fn to add day of week
day_of_week <- function(df) {
  df %>% 
    dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>% 
    dplyr::mutate(day_of_week = dplyr::case_when(
      day_of_week %in% c("Sat", "Sun") ~ "weekend",
      day_of_week %in% c("Mon") ~ "monday",
      !(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
    ) %>% 
      factor(levels = c("rest_of_week", "monday", "weekend")))
}

Custom color palettes, color scales, and vectors of colors:


pal <- c("#006212",
         "#ae3cab",
         "#00db90",
         "#960c00",
         "#55aaff",
         "#ff7e78",
         "#00388d")

age.pal <- viridis::viridis(3,begin = 0.1, end = 0.7)

3 Comparison with deaths time series

3.1 Outline

We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.

Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.

3.2 Lagged correlation

We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.

First we join the NHS Pathways and death data, and aggregate over all England:

## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max

dth_trunc <- dth %>%
  rename(date = date_report) %>%
  filter(date <= trunc_date) 

## join with notification data
all_data <- x %>% 
  filter(!is.na(nhs_region)) %>%
  group_by(date, nhs_region) %>%
  summarise(count = sum(count, na.rm = T)) %>%
  ungroup %>%
  inner_join(dth_trunc,
             by = c("date","nhs_region"))

all_tot <- all_data %>%
  group_by(date) %>%
  summarise(count = sum(count, na.rm = TRUE),
            deaths = sum(deaths, na.rm = TRUE)) 

We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:


## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
  
  ## lag reports
  summary <- all_tot %>% 
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI
    getboot(.) %>%
    mutate(lag = i)

  lag_cor <- bind_rows(lag_cor, summary)
}

cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
  theme_bw() +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_point() +
  geom_line() +
  labs(x = "Lag between NHS pathways and death data (days)",
       y = "Pearson's correlation") +
  large_txt
cor_vs_lag


l_opt <- which.max(lag_cor$r)

This analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.


all_tot <- all_tot %>%
  rename(date_death = date) %>%
  mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
         note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
         date_note = lag(date_death,16))

lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")

summary(lag_mod)
## 
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.5040  -1.7684  -0.2302   2.0232   5.9304  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.683e+00  5.388e-02  105.47  < 2e-16 ***
## note_lag    7.235e-06  5.090e-07   14.22 2.48e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 7.719516)
## 
##     Null deviance: 1791.25  on 29  degrees of freedom
## Residual deviance:  216.91  on 28  degrees of freedom
##   (16 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 4

exp(coefficients(lag_mod))
## (Intercept)    note_lag 
##  293.915663    1.000007
exp(confint(lag_mod))
##                  2.5 %     97.5 %
## (Intercept) 264.220481 326.366718
## note_lag      1.000006   1.000008

Rsq(lag_mod)
## [1] 0.8789056

mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])

all_tot_pred <- 
  all_tot %>%
  filter(!is.na(note_lag)) %>%
  mutate(pred = mod_fit$fit,
         pred.se = mod_fit$se.fit,
         low = exp(pred - 1.96*pred.se),
         hi = exp(pred + 1.96*pred.se))


glm_fit <- all_tot_pred %>% 
    filter(!is.na(note_lag)) %>%
  ggplot(aes(x = note_lag, y = deaths)) +
  geom_point() + 
  geom_line(aes(y = exp(pred))) + 
  geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
  theme_bw() +
  labs(y = "Daily number of\ndeaths reported",
       x = "Daily number of NHS Pathways reports") +
  large_txt

glm_fit

4 Supplementary figures

4.1 Serial interval distribution

This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.

SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale, w = 0.5)

SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
                                        meanlog = log(4.7),
                                        sdlog = log(2.9), w = 0.5)

SI_dist1 <- data.frame(x = SI_distribution$r(1e5)) 
SI_dist1 <- count(SI_dist1, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 30, 5)) +
    theme_bw()

SI_dist2 <- data.frame(x = SI_distribution2$r(1e5)) 
SI_dist2 <- count(SI_dist2, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
    theme_bw()


ggpubr::ggarrange(SI_dist1,
                  SI_dist2,
                  nrow = 1,
                  labels = "AUTO") 

4.2 Sensitivity analysis - 7 or 21 days moving window

We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.

First with the 7 days window:

## set moving time window (1/2/3 weeks)
w <- 7

# create empty df
r_all_sliding_7days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
plot_R <- r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_7days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_7days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_7 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

Then with the 21 days window:

## set moving time window (1/2/3 weeks)
w <- 21

# create empty df
r_all_sliding_21days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
# plot
plot_R <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_21days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_21days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_21 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

And we combine both outputs into a single plot:


ggpubr::ggarrange(r_R_7,
                  r_R_21,
                  nrow = 2,
                  labels = "AUTO",
                  common.legend = TRUE,
                  legend = "bottom") 

4.3 Correlation between NHS Pathways reports and deaths by NHS region


lag_cor_reg <- data.frame()

for (i in 0:30) {

  summary <-
    all_data %>%
    group_by(nhs_region) %>%
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI for each region
    group_modify(~getboot(.x)) %>%
    mutate(lag = i)
  
  lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}

cor_vs_lag_reg <- 
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
  geom_point() +
  geom_line() +
  facet_wrap(~nhs_region) +
  scale_color_manual(values = pal) +
  scale_fill_manual(values = pal, guide = F) +  
  theme_bw() +
  labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(override.aes = list(fill = NA)))

cor_vs_lag_reg

5 Export data

We save the tables created during our analysis:


if (!dir.exists("excel_tables")) {
  dir.create("excel_tables")
}


## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")

for (e in tables_to_export) {
  rio::export(get(e),
              file.path("excel_tables",
                        paste0(e, ".xlsx")))
}

## also export result from regression on lagged data 
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))

6 System information

6.1 Outline

The following information documents the system on which the document was compiled.

6.2 System

This provides information on the operating system.

Sys.info()
##                                                                                             sysname 
##                                                                                            "Darwin" 
##                                                                                             release 
##                                                                                            "19.4.0" 
##                                                                                             version 
## "Darwin Kernel Version 19.4.0: Wed Mar  4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64" 
##                                                                                            nodename 
##                                                                                    "Mac-1651.local" 
##                                                                                             machine 
##                                                                                            "x86_64" 
##                                                                                               login 
##                                                                                              "root" 
##                                                                                                user 
##                                                                                            "runner" 
##                                                                                      effective_user 
##                                                                                            "runner"

6.3 R environment

This provides information on the version of R used:

R.version
##                _                           
## platform       x86_64-apple-darwin15.6.0   
## arch           x86_64                      
## os             darwin15.6.0                
## system         x86_64, darwin15.6.0        
## status                                     
## major          3                           
## minor          6.3                         
## year           2020                        
## month          02                          
## day            29                          
## svn rev        77875                       
## language       R                           
## version.string R version 3.6.3 (2020-02-29)
## nickname       Holding the Windsock

6.4 R packages

This provides information on the packages used:

sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggnewscale_0.4.1     ggpubr_0.3.0         lubridate_1.7.8     
##  [4] chngpt_2020.5-21     cyphr_1.1.0          DT_0.13             
##  [7] kableExtra_1.1.0     janitor_2.0.1        remotes_2.1.1       
## [10] projections_0.4.1    earlyR_0.0.1         epitrix_0.2.2       
## [13] distcrete_1.0.3      incidence_1.7.1      rio_0.5.16          
## [16] reshape2_1.4.4       rvest_0.3.5          xml2_1.3.2          
## [19] linelist_0.0.40.9000 forcats_0.5.0        stringr_1.4.0       
## [22] dplyr_0.8.5          purrr_0.3.4          readr_1.3.1         
## [25] tidyr_1.1.0          tibble_3.0.1         ggplot2_3.3.0       
## [28] tidyverse_1.3.0      here_0.1             reportfactory_0.0.5 
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.4-1  selectr_0.4-2     ggsignif_0.6.0    ellipsis_0.3.1   
##  [5] rprojroot_1.3-2   snakecase_0.11.0  fs_1.4.1          rstudioapi_0.11  
##  [9] farver_2.0.3      fansi_0.4.1       splines_3.6.3     knitr_1.28       
## [13] jsonlite_1.6.1    broom_0.5.6       dbplyr_1.4.3      compiler_3.6.3   
## [17] httr_1.4.1        backports_1.1.7   assertthat_0.2.1  Matrix_1.2-18    
## [21] cli_2.0.2         htmltools_0.4.0   prettyunits_1.1.1 tools_3.6.3      
## [25] gtable_0.3.0      glue_1.4.1        Rcpp_1.0.4.6      carData_3.0-4    
## [29] cellranger_1.1.0  vctrs_0.3.0       nlme_3.1-144      matchmaker_0.1.1 
## [33] crosstalk_1.1.0.1 xfun_0.14         ps_1.3.3          openxlsx_4.1.5   
## [37] lifecycle_0.2.0   rstatix_0.5.0     MASS_7.3-51.5     scales_1.1.1     
## [41] hms_0.5.3         sodium_1.1        yaml_2.2.1        curl_4.3         
## [45] gridExtra_2.3     stringi_1.4.6     kyotil_2019.11-22 boot_1.3-24      
## [49] pkgbuild_1.0.8    zip_2.0.4         rlang_0.4.6       pkgconfig_2.0.3  
## [53] evaluate_0.14     lattice_0.20-38   labeling_0.3      htmlwidgets_1.5.1
## [57] cowplot_1.0.0     processx_3.4.2    tidyselect_1.1.0  plyr_1.8.6       
## [61] magrittr_1.5      R6_2.4.1          generics_0.0.2    DBI_1.1.0        
## [65] pillar_1.4.4      haven_2.3.0       foreign_0.8-75    withr_2.2.0      
## [69] mgcv_1.8-31       survival_3.1-8    abind_1.4-5       modelr_0.1.8     
## [73] crayon_1.3.4      car_3.0-8         utf8_1.1.4        rmarkdown_2.1    
## [77] viridis_0.5.1     grid_3.6.3        readxl_1.3.1      data.table_1.12.8
## [81] callr_3.4.3       reprex_0.3.0      digest_0.6.25     webshot_0.5.2    
## [85] munsell_0.5.0     viridisLite_0.3.0